An Empirical Study on the Factors Affecting Savings Bank Loan Interest Rates
Bibliographic record
Abstract
The study analyzed reciprocal effects of base rate of the central bank, CD, CP, interest rates for bank deposits, bank loan interest, interest rate for savings bank deposits and savings bank loan interest. In particular, the study attempted to find an interest rate which affects interest rates of savings banks. If these variables affected interest rate decisions by savings banks, it would be possible to predict that movements of these variables in the future may affect changes of interest rates as well. In the impulse reaction function, CD rate and CP rate took the highest impact to the savings bank’s deposit interest and loan rate. In forecast error variance decomposition analysis, it was fond that the deposit interest and loan rate of the savings bank reciprocally affected each other. Then, CD rate and CP rate had the highest explanation power. Consequently, factors that affect the savings’ bank’s decision on interest rates were a bank’s deposit interest, CP and RP interest rate and RP rate which is the benchmark interest rate. The important point is that RP rate as the benchmark interest rate takes an important role because it becomes the base for influenced variables.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".